EP2884431A1 - Verfahren zur Konfiguration eines Systems von Systemen - Google Patents

Verfahren zur Konfiguration eines Systems von Systemen Download PDF

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Publication number
EP2884431A1
EP2884431A1 EP13196458.7A EP13196458A EP2884431A1 EP 2884431 A1 EP2884431 A1 EP 2884431A1 EP 13196458 A EP13196458 A EP 13196458A EP 2884431 A1 EP2884431 A1 EP 2884431A1
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Prior art keywords
systems
configuration
membrane
membranes
phase
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EP13196458.7A
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English (en)
French (fr)
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Deepak Dhungana
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Siemens AG Oesterreich
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Siemens AG Oesterreich
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming

Definitions

  • the invention relates to a method for the configuration of a system-of-systems.
  • Emerging applications are not just large-scale and complex; they are also characterized by decentralized, distributed, networked compositions of heterogeneous and (semi)autonomous elements. These new “systems” are, in fact, “systems-of-systems”. The term has arisen from the systems engineering community and reflects the interest in concepts and developments such as smart grids, integrated supply chains, collaborative enterprises, and next-generation air traffic management.
  • a system-of-systems is a collection of independent systems that work together to create a new, more complex system which offers more functionality and performance than simply the sum of the constituent systems.
  • a system-of-systems is a task-oriented or dedicated configuration of a set of component systems that are available to deal with a certain task. Configuration of a system-of-systems is the task of selecting the optimal subset of component systems, that can perform the required task.
  • the domain (set of all possible values) for each attribute is also considered to be given.
  • Each system can be characterized by a finite set of such characteristics.
  • Each component system possesses a subset of System-of-systems characteristics, meaning that can provide services related to one particular area or aspect of the overall System-of-systems functionality.
  • C S i C SoS
  • impulse can be specified using a set of characteristics.
  • C(R) be the set of characteristics of a required impulse execution System-of-systems (SoS fulfilling the input requirements).
  • Membrane computing is a variation of the so called P-system, a computational model in the field of computer science that performs calculations using a biologically-inspired process.
  • a P-system is defined as a series of membranes containing chemicals (in finite quantities), catalysts and rules which determine possible ways in which chemicals may react with one another to form products. Rules may also cause chemicals to pass through membranes or even cause membranes to dissolve.
  • FIG. 1 An exemplary embodiment of the invention is shown in Fig. 1
  • Scoping is understood as an activity that bounds a system or set of systems by defining those behaviors or aspects that are “in”' and those behaviors or aspects that are “out”. In the context of system-of-systems it means to identify component systems that could potentially be part of any impulse execution system-of-systems.
  • each identified component system describes its role by specifying a series of participation statements.
  • Participation statements can be seen as descriptions of what the system can perform and conditions under which the systems can or need to be integrated and constraints related to services provided by other potential systems.
  • the following table presents a few examples of such participation statements for the system-of-systems product line of a catastrophe management system. These statements are domain-specific viewpoints of the stakeholders on the overall functionality of the system-of-systems.
  • the component systems are then characterized based on the system-of-systems attributes.
  • the answers to the extracted attributes are present in the participation statements themselves. So by providing a value to each related attribute, the participating systems can be characterized.
  • the result is a system-of-systems knowledge base.
  • the individual systems and the variability of their integration in the system-of-systems is reduced to the set of characteristics. These characteristics are refined and updated as the system is used -- more and more participation statements can be added, leading to more finer granularity of configuration options.
  • the configuration impulse - is defined, that needs to be fulfilled by the configured system-of-systems. In other words, it is a description of the system regarded.
  • the configuration impulse based on the known attributes of the System-of-systems will be characterized.
  • configuring System-of-systems is about finding a set of component systems that have the same characteristics as specified by the configured impulse.
  • the characteristics of the impulse and the characteristics of the system-of-systems are then fed into a membrane-based solver.
  • the result is the required impulse execution System-of-systems.
  • Core of the present invention is the use of a new kind of solver that can be adopted to calculate the optimal impulse execution system-of-systems.
  • the solver is constructed using membranes, also called P-Systems, and uses metaphors of chemical reactions occurring in living cells, as for example described in "Gheorghe P aun, Computing with Membranes, Journal of Computer and System Sciences, Volume 61, Issue 1 , August 2000, Pages 108-143"
  • P-Systems are devoted to abstract computing ideas from the structure and the functioning of living cells. They are parallel, distributed computing models, processing multisets of symbols in compartments.
  • a variant of P-Systems with active membranes is used.
  • membranes play an important role in the reactions which take place in a cell, they can evolve themselves, either changing their characteristics or even getting divided. These properties are exploited to create an exponential workspace in linear time allowing to solve the NP-complete configuration problem in linear time. See also " M. J. Perez-Jimenez and A. Riscos-Nunez. A linear-time solution to the knapsack problem using P-systems with active membranes. In Workshop on Membrane Computing, pages 250--268, 2003 ".This kind of break-through is possible because membranes are treated parallel computing devices and it is possible to create exponential number of membranes in linear time.
  • the essential ingredient of a P-system is its membrane structure.
  • Membranes are three-dimensional vesicles, dividing the cell in different compartments.
  • the compartments of a cell contains substances (ions, small molecules, macromolecules) swimming in an aqueous solution.
  • the objects evolve according to given rules; the objects can pass through membranes, the membranes can also dissolve or divide.
  • the evolution rules are localized or associated with the regions or the membranes of the system.
  • the membranes play an important role in the reactions which take place in a cell.
  • the molecules swimming inside the membranes are represented by multisets.
  • a multiset can be represented in many ways, but the most compact one is in the form of a string. For instance, if the objects a, b, c are present in, respectively, 5, 2, 6 copies each, we can represent this multiset by the string a 5 b 2 c 6 ; of course, all permutations of this string represent the same multiset.
  • Multiset-rewriting rules can be compared to reaction equations as customarily used in chemistry and biochemistry.
  • the communication rules (antiport and symport rules) are used to pass the chemicals through the membranes.
  • This model of computing reflects similar dynamics like real-world S-o-S.
  • m ⁇ 1 is the initial number of membranes (also known as the degree of the system)
  • 2.0 is the alphabet of the objects, representing the chemical substances, that can swim around 3.
  • H is a finite set of labels for the membranes 4.
  • ⁇ is the membrane structure, consisting of m membranes having initially neutral polarizations, labeled with elements of H.
  • the hierarchical structure of the membrane is represented in a textual form.
  • the string [ a [ b [ e [ g ]g [ f ] f ] e ] b [ c ] c [ d ] d ] a represents the membrane structure given below.
  • the ordering and position of the membranes in the same compartment makes no difference. 5.
  • w 1 , w 2 ,..., w m are strings over O, describing the multiset of objects places in the m regions of ⁇ 6.
  • R is a set of developmental rules of the following forms (table 2.): Table 2 r1: a h ⁇ ⁇ h e , These are object evolution rules, associated with membranes and depending on the label and the charge of the membranes, but not directly involving the membranes (i.e., membranes do not take part in the application of these rules). If this rule is applied to a membrane called h, then an object a evolves to a multiset v.
  • the objects in the membrane may change during the process from a to b. for h ⁇ H , e ⁇ Pol , a , b ⁇ O r5: a h h e 1 ⁇ b h h e 2 , c h h e 3 ,
  • the objects in a P-system evolve in the maximally parallel manner. Just like chemical reactions inside the membranes of a cell, there is no priority among the rules. If more than one rule can be applied at one time, then one of them is randomly selected.
  • the number of membranes may increase if rules of type r5 are applied; and decrease if rules of type r4 are applied.
  • the increase of membranes can be exponentially high in a linear number of steps: using successively a division rule, due to the maximal parallelism, in n steps, 2 n copies of the same membrane are obtained.
  • Example Input Let consider a System-of-systems with 5 component systems ( S 1 ,..., S 5 ) and a total of 4 Boolean attributes ( A 1 ,..., A 4 ), i.e., the domain of each attribute is ⁇ T , F ⁇ . This means there are total 8 System-of-systems characteristics. Let assume that we require a set of systems specified by ⁇ ( A 1 , T ), ( A 2 , T ), ( A 3 , F ) ⁇ .
  • the output is a set of integers which represents a subset of S (the elements of the set being indices of the systems included in the subset). For example, the output for the problem described above would be ⁇ 1, 5 ⁇ , meaning S1 and S5 represent the solution for the given input.
  • the solver consists of two membranes, one outer membrane s and an inner membrane e
  • the computation consists of four phases (these phases run in parallel). Lets consider a computation, where there are n systems, q characteristics in the configuration impulse.
  • Preparation Phase In the first phase of computation, a series of membrane division rules are applied to create a membrane for every subset of S. When this preparation phase is complete, each membrane contains molecules representing the set of characteristics ( c 1 ,..., c b ) possessed by the subsystem represented by that membrane.
  • Comparison Phase begins with the introduction of the characteristics of the required system ( r 1 ,..., r q ) to each membrane. These molecules react with the system characteristic molecules present in the membranes to produce "match molecules"' m. If the number of match molecules is equal to number of characteristics of the required system then a candidate for the solution is found.
  • the third phase is related to comparing all the candidate subsystems to identify which one is the most suitable one, i.e., consists of the minimum number of component systems. To find this out, each candidate membrane sends out representative molecules to the skin membrane. The number of representative molecules originating from the candidate membranes is equal to the number of component systems represented by that membrane.
  • membranes play an important role in the reactions which take place in a cell, they can evolve themselves, either changing their characteristics or even getting divided.
  • membranes play an important role in the reactions which take place in a cell, they can evolve themselves, either changing their characteristics or even getting divided.
  • We exploit these properties to create an exponential workspace in linear time allowing us to solve the NP-complete configuration problem in linear time (inspired by [Perez '2003])
  • This kind of break-through is possible because membranes are treated parallel computing devices and it is possible to create exponential number of membranes in linear time.
  • the invention also shows how the different aspects of completely independent systems can be associated with each other through parameter extraction.
  • the integrative nature of the invention can play an important role in modeling systems and systems and understanding dependencies among heterogeneous systems.

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EP13196458.7A 2013-12-10 2013-12-10 Verfahren zur Konfiguration eines Systems von Systemen Ceased EP2884431A1 (de)

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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070094166A1 (en) * 2002-08-05 2007-04-26 Edwin Addison Knowledge-based methods for genetic network analysis and the whole cell computer system based thereon

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070094166A1 (en) * 2002-08-05 2007-04-26 Edwin Addison Knowledge-based methods for genetic network analysis and the whole cell computer system based thereon

Non-Patent Citations (7)

* Cited by examiner, † Cited by third party
Title
DANIEL DIAZ-PERNIL ET AL: "A fast solution to the partition problem by using tissue-like P systems", BIO-INSPIRED COMPUTING: THEORIES AND APPLICATIONS, 2008. BICTA 2008. 3RD INTERNATIONAL CONFERENCE ON, IEEE, PISCATAWAY, NJ, USA, 28 September 2008 (2008-09-28), pages 43 - 48, XP031352122, ISBN: 978-1-4244-2724-6 *
DEEPAK DHUNGANA ET AL: "Generation of conjoint domain models for system-of-systems", PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON GENERATIVE PROGRAMMING: CONCEPTS & EXPERIENCES, GPCE '13, 27 October 2013 (2013-10-27), New York, New York, USA, pages 159 - 168, XP055116641, ISBN: 978-1-45-032373-4, DOI: 10.1145/2517208.2517224 *
GHEORGHE PAUN: "Computing with Membranes", JOURNAL OF COMPUTER AND SYSTEM SCIENCES, vol. 61, no. 1, August 2000 (2000-08-01), pages 108 - 143
LIANG HUANG ET AL: "Dynamic multi-objective optimization based on membrane computing for control of time-varying unstable plants", INFORMATION SCIENCES, AMSTERDAM, NL, vol. 181, no. 11, 31 December 2010 (2010-12-31), pages 2370 - 2391, XP028367411, ISSN: 0020-0255, [retrieved on 20110114], DOI: 10.1016/J.INS.2010.12.015 *
M. J. PEREZ-JIMENEZ; A. RISCOS-NUNEZ: "A linear-time solution to the knapsack problem using P-systems with active membranes", WORKSHOP ON MEMBRANE COMPUTING, 2003, pages 250 - 268
MARIOJ PÃ CR REZ-JIMÃ CR NEZ ET AL: "A Linear-Time Solution to the Knapsack Problem Using P Systems with Active Membranes", 13 January 2004, MEMBRANE COMPUTING; [LECTURE NOTES IN COMPUTER SCIENCE;;LNCS], SPRINGER-VERLAG, BERLIN/HEIDELBERG, PAGE(S) 250 - 268, ISBN: 978-3-540-20895-2, XP019002234 *
VAN NGUYEN ET AL: "Balancing Performance, Flexibility, and Scalability in a Parallel Computing Platform for Membrane Computing Applications", 25 June 2007, MEMBRANE COMPUTING; [LECTURE NOTES IN COMPUTER SCIENCE], SPRINGER BERLIN HEIDELBERG, BERLIN, HEIDELBERG, PAGE(S) 385 - 413, ISBN: 978-3-540-77311-5, XP019085668 *

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